Python metrics.SquadEmAndF1方法代码示例

# 需要导入模块: from allennlp.training import metrics [as 别名]
# 或者: from allennlp.training.metrics import SquadEmAndF1 [as 别名]
def __init__(self, submodels                                  )        :
        super(BidafEnsemble, self).__init__(submodels)

        self._squad_metrics = SquadEmAndF1()

    #overrides 

# 需要导入模块: from allennlp.training import metrics [as 别名]
# 或者: from allennlp.training.metrics import SquadEmAndF1 [as 别名]
def __init__(self, vocab: Vocabulary,
                 text_field_embedder: TextFieldEmbedder,
                 phrase_layer: Seq2SeqEncoder,
                 projected_layer: Seq2SeqEncoder,
                 flow_layer: Seq2SeqEncoder,
                 contextual_passage: Seq2SeqEncoder,
                 contextual_question: Seq2SeqEncoder,
                 dropout: float = 0.2,
                 regularizer: Optional[RegularizerApplicator] = None,
                 initializer: InitializerApplicator = InitializerApplicator(),
                 ):

        super(MultiGranularityHierarchicalAttentionFusionNetworks, self).__init__(vocab, regularizer)
        self._text_field_embedder = text_field_embedder
        self._phrase_layer = phrase_layer
        self._encoding_dim = self._phrase_layer.get_output_dim()
        self.projected_layer = torch.nn.Linear(self._encoding_dim + 1024, self._encoding_dim)
        self.fuse = FusionLayer(self._encoding_dim)
        self.projected_lstm = projected_layer
        self.flow = flow_layer
        self.contextual_layer_p = contextual_passage
        self.contextual_layer_q = contextual_question
        self.linear_self_align = torch.nn.Linear(self._encoding_dim, 1)
        self.bilinear_layer_s = BilinearSeqAtt(self._encoding_dim, self._encoding_dim)
        self.bilinear_layer_e = BilinearSeqAtt(self._encoding_dim, self._encoding_dim)
        self.yesno_predictor = torch.nn.Linear(self._encoding_dim, 3)
        self.relu = torch.nn.ReLU()

        self._max_span_length = 30

        self._span_start_accuracy = CategoricalAccuracy()
        self._span_end_accuracy = CategoricalAccuracy()
        self._span_accuracy = BooleanAccuracy()
        self._squad_metrics = SquadEmAndF1()
        self._span_yesno_accuracy = CategoricalAccuracy()
        self._official_f1 = Average()
        self._variational_dropout = InputVariationalDropout(dropout)

        self._loss = torch.nn.CrossEntropyLoss()
        initializer(self) 

# 需要导入模块: from allennlp.training import metrics [as 别名]
# 或者: from allennlp.training.metrics import SquadEmAndF1 [as 别名]
def __init__(self,
                 vocab: Vocabulary,
                 text_field_embedder: TextFieldEmbedder,
                 question_encoder: Seq2SeqEncoder,
                 passage_encoder: Seq2SeqEncoder,
                 pair_encoder: AttentionEncoder,
                 self_encoder: AttentionEncoder,
                 output_layer: QAOutputLayer,
                 initializer: InitializerApplicator = InitializerApplicator(),
                 regularizer: Optional[RegularizerApplicator] = None,
                 share_encoder: bool = False):

        super().__init__(vocab, regularizer)
        self.text_field_embedder = text_field_embedder
        self.question_encoder = question_encoder
        self.passage_encoder = passage_encoder
        self.pair_encoder = pair_encoder
        self.self_encoder = self_encoder
        self.output_layer = output_layer

        self._span_start_accuracy = CategoricalAccuracy()
        self._span_end_accuracy = CategoricalAccuracy()
        self._span_accuracy = BooleanAccuracy()
        self._squad_metrics = SquadEmAndF1()
        self.share_encoder = share_encoder
        self.loss = torch.nn.CrossEntropyLoss()
        initializer(self) 

# 需要导入模块: from allennlp.training import metrics [as 别名]
# 或者: from allennlp.training.metrics import SquadEmAndF1 [as 别名]
def __init__(self, vocab            ,
                 text_field_embedder                   ,
                 num_highway_layers     ,
                 phrase_layer                ,
                 similarity_function                    ,
                 modeling_layer                ,
                 span_end_encoder                ,
                 dropout        = 0.2,
                 mask_lstms       = True,
                 initializer                        = InitializerApplicator(),
                 regularizer                                  = None)        :
        super(BidirectionalAttentionFlow, self).__init__(vocab, regularizer)

        self._text_field_embedder = text_field_embedder
        self._highway_layer = TimeDistributed(Highway(text_field_embedder.get_output_dim(),
                                                      num_highway_layers))
        self._phrase_layer = phrase_layer
        self._matrix_attention = LegacyMatrixAttention(similarity_function)
        self._modeling_layer = modeling_layer
        self._span_end_encoder = span_end_encoder

        encoding_dim = phrase_layer.get_output_dim()
        modeling_dim = modeling_layer.get_output_dim()
        span_start_input_dim = encoding_dim * 4 + modeling_dim
        self._span_start_predictor = TimeDistributed(torch.nn.Linear(span_start_input_dim, 1))

        span_end_encoding_dim = span_end_encoder.get_output_dim()
        span_end_input_dim = encoding_dim * 4 + span_end_encoding_dim
        self._span_end_predictor = TimeDistributed(torch.nn.Linear(span_end_input_dim, 1))

        # Bidaf has lots of layer dimensions which need to match up - these aren't necessarily
        # obvious from the configuration files, so we check here.
        check_dimensions_match(modeling_layer.get_input_dim(), 4 * encoding_dim,
                               u"modeling layer input dim", u"4 * encoding dim")
        check_dimensions_match(text_field_embedder.get_output_dim(), phrase_layer.get_input_dim(),
                               u"text field embedder output dim", u"phrase layer input dim")
        check_dimensions_match(span_end_encoder.get_input_dim(), 4 * encoding_dim + 3 * modeling_dim,
                               u"span end encoder input dim", u"4 * encoding dim + 3 * modeling dim")

        self._span_start_accuracy = CategoricalAccuracy()
        self._span_end_accuracy = CategoricalAccuracy()
        self._span_accuracy = BooleanAccuracy()
        self._squad_metrics = SquadEmAndF1()
        if dropout > 0:
            self._dropout = torch.nn.Dropout(p=dropout)
        else:
            self._dropout = lambda x: x
        self._mask_lstms = mask_lstms

        initializer(self) 

# 需要导入模块: from allennlp.training import metrics [as 别名]
# 或者: from allennlp.training.metrics import SquadEmAndF1 [as 别名]
def __init__(self, vocab: Vocabulary,
                 text_field_embedder: TextFieldEmbedder,
                 phrase_layer: Seq2SeqEncoder,
                 residual_encoder: Seq2SeqEncoder,
                 span_start_encoder: Seq2SeqEncoder,
                 span_end_encoder: Seq2SeqEncoder,
                 initializer: InitializerApplicator,
                 dropout: float = 0.2,
                 pair2vec_dropout: float = 0.15,
                 max_span_length: int = 30,
                 pair2vec_model_file: str = None,
                 pair2vec_config_file: str = None
                 ) -> None:
        super().__init__(vocab)
        self._max_span_length = max_span_length
        self._text_field_embedder = text_field_embedder
        self._phrase_layer = phrase_layer
        self._encoding_dim = phrase_layer.get_output_dim()

        self.pair2vec = pair2vec_util.get_pair2vec(pair2vec_config_file, pair2vec_model_file)
        self._pair2vec_dropout = torch.nn.Dropout(pair2vec_dropout)

        self._matrix_attention = LinearMatrixAttention(self._encoding_dim, self._encoding_dim, 'x,y,x*y')

        # atten_dim = self._encoding_dim * 4 + 600 if ablation_type == 'attn_over_rels' else self._encoding_dim * 4
        atten_dim = self._encoding_dim * 4 + 600
        self._merge_atten = TimeDistributed(torch.nn.Linear(atten_dim, self._encoding_dim))

        self._residual_encoder = residual_encoder

        self._self_attention = LinearMatrixAttention(self._encoding_dim, self._encoding_dim, 'x,y,x*y')

        self._merge_self_attention = TimeDistributed(torch.nn.Linear(self._encoding_dim * 3,
                                                                     self._encoding_dim))

        self._span_start_encoder = span_start_encoder
        self._span_end_encoder = span_end_encoder

        self._span_start_predictor = TimeDistributed(torch.nn.Linear(self._encoding_dim, 1))
        self._span_end_predictor = TimeDistributed(torch.nn.Linear(self._encoding_dim, 1))
        self._squad_metrics = SquadEmAndF1()
        initializer(self)

        self._span_start_accuracy = CategoricalAccuracy()
        self._span_end_accuracy = CategoricalAccuracy()
        self._official_em = Average()
        self._official_f1 = Average()

        self._span_accuracy = BooleanAccuracy()
        self._variational_dropout = InputVariationalDropout(dropout) 

# 需要导入模块: from allennlp.training import metrics [as 别名]
# 或者: from allennlp.training.metrics import SquadEmAndF1 [as 别名]
def __init__(self, vocab: Vocabulary,
                 elmo_embedder: TextFieldEmbedder,
                 tokens_embedder: TextFieldEmbedder,
                 features_embedder: TextFieldEmbedder,
                 phrase_layer: Seq2SeqEncoder,
                 projected_layer: Seq2SeqEncoder,
                 contextual_passage: Seq2SeqEncoder,
                 contextual_question: Seq2SeqEncoder,
                 dropout: float = 0.2,
                 regularizer: Optional[RegularizerApplicator] = None,
                 initializer: InitializerApplicator = InitializerApplicator(),
                 ):

        super(MultiGranularityHierarchicalAttentionFusionNetworks, self).__init__(vocab, regularizer)
        self.elmo_embedder = elmo_embedder
        self.tokens_embedder = tokens_embedder
        self.features_embedder = features_embedder
        self._phrase_layer = phrase_layer
        self._encoding_dim = self._phrase_layer.get_output_dim()
        self.projected_layer = torch.nn.Linear(self._encoding_dim + 1024, self._encoding_dim)
        self.fuse_p = FusionLayer(self._encoding_dim)
        self.fuse_q = FusionLayer(self._encoding_dim)
        self.fuse_s = FusionLayer(self._encoding_dim)
        self.projected_lstm = projected_layer
        self.contextual_layer_p = contextual_passage
        self.contextual_layer_q = contextual_question
        self.linear_self_align = torch.nn.Linear(self._encoding_dim, 1)
        # self._self_attention = LinearMatrixAttention(self._encoding_dim, self._encoding_dim, 'x,y,x*y')
        self._self_attention = BilinearMatrixAttention(self._encoding_dim, self._encoding_dim)
        self.bilinear_layer_s = BilinearSeqAtt(self._encoding_dim, self._encoding_dim)
        self.bilinear_layer_e = BilinearSeqAtt(self._encoding_dim, self._encoding_dim)
        self.yesno_predictor = FeedForward(self._encoding_dim, self._encoding_dim, 3)
        self.relu = torch.nn.ReLU()

        self._max_span_length = 30

        self._span_start_accuracy = CategoricalAccuracy()
        self._span_end_accuracy = CategoricalAccuracy()
        self._span_accuracy = BooleanAccuracy()
        self._squad_metrics = SquadEmAndF1()
        self._span_yesno_accuracy = CategoricalAccuracy()
        self._official_f1 = Average()
        self._variational_dropout = InputVariationalDropout(dropout)

        self._loss = torch.nn.CrossEntropyLoss()
        initializer(self) 

# 需要导入模块: from allennlp.training import metrics [as 别名]
# 或者: from allennlp.training.metrics import SquadEmAndF1 [as 别名]
def __init__(self, vocab: Vocabulary,
                 text_field_embedder: TextFieldEmbedder,
                 phrase_layer: Seq2SeqEncoder,
                 projected_layer: Seq2SeqEncoder,
                 contextual_passage: Seq2SeqEncoder,
                 contextual_question: Seq2SeqEncoder,
                 dropout: float = 0.2,
                 regularizer: Optional[RegularizerApplicator] = None,
                 initializer: InitializerApplicator = InitializerApplicator(),
                 ):

        super(MultiGranularityHierarchicalAttentionFusionNetworks, self).__init__(vocab, regularizer)
        self._text_field_embedder = text_field_embedder
        self._phrase_layer = phrase_layer
        self._encoding_dim = self._phrase_layer.get_output_dim()
        self.projected_layer = torch.nn.Linear(self._encoding_dim + 1024, self._encoding_dim)
        self.fuse_p = FusionLayer(self._encoding_dim)
        self.fuse_q = FusionLayer(self._encoding_dim)
        self.fuse_s = FusionLayer(self._encoding_dim)
        self.projected_lstm = projected_layer
        self.contextual_layer_p = contextual_passage
        self.contextual_layer_q = contextual_question
        self.linear_self_align = torch.nn.Linear(self._encoding_dim, 1)
        # self.bilinear_self_align = BilinearSelfAlign(self._encoding_dim)
        # self._self_attention = LinearMatrixAttention(self._encoding_dim, self._encoding_dim, 'x,y,x*y')
        self._self_attention = BilinearMatrixAttention(self._encoding_dim, self._encoding_dim)
        self.bilinear_layer_s = BilinearSeqAtt(self._encoding_dim, self._encoding_dim)
        self.bilinear_layer_e = BilinearSeqAtt(self._encoding_dim, self._encoding_dim)
        self.yesno_predictor = torch.nn.Linear(self._encoding_dim, 3)
        self.relu = torch.nn.ReLU()

        self._max_span_length = 30

        self._span_start_accuracy = CategoricalAccuracy()
        self._span_end_accuracy = CategoricalAccuracy()
        self._span_accuracy = BooleanAccuracy()
        self._squad_metrics = SquadEmAndF1()
        self._span_yesno_accuracy = CategoricalAccuracy()
        self._official_f1 = Average()
        self._variational_dropout = InputVariationalDropout(dropout)

        self._loss = torch.nn.CrossEntropyLoss()
        initializer(self)